@inproceedings{pandey-etal-2022-multilinguals,
    title = "Multilinguals at {S}em{E}val-2022 Task 11: Complex {NER} in Semantically Ambiguous Settings for Low Resource Languages",
    author = "Pandey, Amit  and
      Daw, Swayatta  and
      Unnam, Narendra  and
      Pudi, Vikram",
    editor = "Emerson, Guy  and
      Schluter, Natalie  and
      Stanovsky, Gabriel  and
      Kumar, Ritesh  and
      Palmer, Alexis  and
      Schneider, Nathan  and
      Singh, Siddharth  and
      Ratan, Shyam",
    booktitle = "Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022)",
    month = jul,
    year = "2022",
    address = "Seattle, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/Add-Cong-Liu-Florida-Atlantic-University-author-id/2022.semeval-1.201/",
    doi = "10.18653/v1/2022.semeval-1.201",
    pages = "1469--1476",
    abstract = "We leverage pre-trained language models to solve the task of complex NER for two low-resource languages: Chinese and Spanish. We use the technique of Whole Word Masking (WWM) to boost the performance of masked language modeling objective on large and unsupervised corpora. We experiment with multiple neural network architectures, incorporating CRF, BiLSTMs, and Linear Classifiers on top of a fine-tuned BERT layer. All our models outperform the baseline by a significant margin and our best performing model obtains a competitive position on the evaluation leaderboard for the blind test set."
}